Method and system for enhancement of slow wave activity and personalized measurement thereof

ABSTRACT

The present disclosure pertains to enhancement of slow wave activity and personalized measurement thereof. Sensory stimulation may be delivered to a subject upon detection of slow wave activity of the subject. The system may obtain a baseline model, which includes baseline information describing slow wave activity increases due to sensory stimulation delivered to an age matched population. The system may generate a personalized model based on the baseline information, the sensory stimulation delivered to the subject, and slow wave activity increases due to sensory stimulation delivered to the subject during prior sleep sessions. The system may then provide the subject with personalized measurements relating to the slow wave activity enhancement.

CROSS-REFERENCE TO PRIOR APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/945,476, filed on 9 Dec. 2019. This application is herebyincorporated by reference herein.

BACKGROUND 1. Field

The present disclosure pertains to a system and method for enhancingslow wave activity and providing a personalized measurement thereof.

2. Description of the Related Art

Systems for monitoring sleep and delivering sensory stimulation tosubjects during sleep are known. Electroencephalogram (EEG) sensor-basedsleep monitoring and sensory stimulation systems are known.

SUMMARY

Systems and methods described herein may provide enhancements to theslow wave activity of a subject and personalized measurements thereof.Accordingly, one or more aspects of the present disclosure relate to asystem configured to measure slow wave activity of a subject during asleep session. The system comprises one or more sensors, one or moresensory stimulators, one or more processors, and/or other components.The one or more sensors are configured to generate output signalsconveying information related to brain activity of the subject duringthe sleep session. The one or more sensory stimulators are configured toprovide the sensory stimulation to the subject during the sleep session.The one or more processors are coupled to the one or more sensors andthe one or more sensory stimulators. The one or more processors areconfigured by machine-readable instructions. The one or more processorsare configured to control the one or more sensory stimulators based onstimulation parameters.

In some embodiments, the one or more sensors comprise one or moreelectroencephalogram (EEG) electrodes configured to generate theinformation related to brain activity. In some embodiments, the one ormore processors are further configured to detect deep sleep in thesubject. In some embodiments, the one or more processors are configuredto determine that the subject has remained in deep sleep for acontinuous threshold amount of time during the sleep session. In someembodiments, the one or more processors are further configured toestimate the likelihood of sleep micro-arousals.

In some embodiments, detecting deep sleep comprises causing a deeplearning algorithm to be trained based on the information related to thebrain activity of the subject, as captured by the EEG electrodes. Insome embodiments, based on the output signals, the trained deep learningalgorithm may determine periods when the subject is experiencing deepsleep during the sleep session. The trained deep learning algorithmcomprises an input layer, an output layer, and one or more intermediatelayers between the input layer and the output layer.

In some embodiments, the one or more processors are configured suchthat, once deep sleep is detected and the likelihood of sleepmicro-arousals is below a threshold, the processors apply stimulationsto the subject. In some embodiments, the stimulations may be repeatingvibrations, constant vibration, repeating light pulses, constant lightstimulation, and/or other repeating or constant stimulations. In someembodiments, repeating stimulations are separated from one another by aconstant interval. In some embodiments, the intensity of thestimulations is based upon the depth of sleep.

In some embodiments, the one or more processors are configured to detectslow wave activity in the subject during the sleep session. The one ormore processors may determine an increase in slow wave activity of thesubject throughout the sleep session, where the increase is caused bythe sensory stimulation provided to the subject. The increase in slowwave activity is determined based on a baseline model and a personalizedmodel. In some embodiments, the baseline model may describe increases inslow wave activity in a population of subjects (e.g., an age-matchedpopulation) as a function of sensory stimulation provided to thepopulation of subjects. In some embodiments, the personalized model mayutilize slow wave activity for the subject measured during prior sleepsessions in which the sensory stimulation was provided to the subject,as well as information from the baseline model. In some embodiments, thepersonalized model may be modified based on the baseline model and theslow wave activity as measured by the one or more sensors during thesleep session.

In some embodiments, the one or more processors may provide personalizedmeasurements to the subject following the sleep session based on thebaseline model and the modified personalized model. In some embodiments,the sleep quality feedback may comprise a “boost” calculation, whichindicates the sleep quality benefit derived from receiving thestimulations during the sleep sessions. In some embodiments, the boostcalculation comprises information about slow wave activity enhancementfor the age matched population of subjects and information about slowwave activity enhancement for the subject. In some embodiments, thesleep quality feedback may also comprise a score that accounts for othersleep factors. The sleep quality feedback may combine the score and theboost in order to provide the subject with overall quantitative sleepquality feedback for the sleep session.

These and other objects, features, and characteristics of the presentdisclosure, as well as the methods of operation and functions of therelated elements of structure and the combination of parts and economiesof manufacture, will become more apparent upon consideration of thefollowing description and the appended claims with reference to theaccompanying drawings, all of which form a part of this specification,wherein like reference numerals designate corresponding parts in thevarious figures. It is to be expressly understood, however, that thedrawings are for the purpose of illustration and description only andare not intended as a definition of the limits of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of a system configured to deliversensory stimulation to a subject during a sleep session, in accordancewith one or more embodiments.

FIG. 2 illustrates several of the operations performed by the system, inaccordance with one or more embodiments.

FIG. 3 illustrates slow wave activity enhancement for a subject during asleep session, in accordance with one or more embodiments.

FIG. 4 illustrates contributing factors for sleep quality feedback, inaccordance with one or more embodiments.

FIG. 5 illustrates components of a sleep boost calculation, inaccordance with one or more embodiments.

FIG. 6 illustrates a method for measuring slow wave activity of asubject during a sleep session, in accordance with one or moreembodiments.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

As used herein, the singular form of “a”, “an”, and “the” include pluralreferences unless the context clearly dictates otherwise. As usedherein, the term “or” means “and/or” unless the context clearly dictatesotherwise. As used herein, the statement that two or more parts orcomponents are “coupled” shall mean that the parts are joined or operatetogether either directly or indirectly, i.e., through one or moreintermediate parts or components, so long as a link occurs. As usedherein, “directly coupled” means that two elements are directly incontact with each other. As used herein, “fixedly coupled” or “fixed”means that two components are coupled to move as one while maintaining aconstant orientation relative to each other.

Directional phrases used herein, such as, for example and withoutlimitation, top, bottom, left, right, upper, lower, front, back, andderivatives thereof, relate to the orientation of the elements shown inthe drawings and are not limiting upon the claims unless expresslyrecited therein.

FIG. 1 is a schematic illustration of a system 10 configured to measureslow wave activity of a subject 12 during a sleep session. System 10 isconfigured to measure an increase in slow wave activity of the subjectduring the sleep session based on a baseline model and a personalizedmodel to provide the subject with sleep quality feedback and/or forother purposes. System 10 is configured such that sensory stimulation,which may include auditory, haptic, light, and/or other stimulation, isdelivered during sleep. In some embodiments, the stimulation is onlydelivered to the subject when processors in system 10 (described below)have determined that subject 12 is in deep sleep and that the likelihoodof micro-arousals is low (e.g., below a threshold). In some embodiments,system 10 delivers stimulations to subject 12 (e.g., vibrations and/orlight pulses). In some embodiments, the stimulations may be repeatingstimulations (e.g., repeating vibrations and/or repeating light pulses)and/or constant stimulations delivered to the subject for the durationof the deep sleep period. As described herein, the one or moreprocessors may adjust the intensity of the stimulations based on thedepth of sleep (i.e., as sleep becomes deeper the one or more processorsincrease the intensity of the stimulations). The one or more processorsmay then determine an increase in slow wave activity caused by thesensory stimulations provided to subject 12. The increase may be basedupon the baseline model and the personalized model. In some embodiments,the baseline model describes increases in slow wave activity in an agematched population of subjects as a function of sensory stimulationprovided to the age matched population of subjects. In some embodiments,the personalized model utilizes and/or is based on slow wave activityfor subject 12 measured during prior sleep sessions in which the sensorystimulation was provided to subject 12. In some embodiments, the one ormore processors may modify the personalized model based on the baselinemodel and the slow wave activity as measured by the one or more sensorsduring the sleep session. The one or more processors may then use themodified personalized model subsequently (e.g., later in the sleepsession and/or in subsequent sleep sessions).

Providing accurate sleep quality feedback to subject 12 which accountsfor enhancement due to sensory stimulation is important to subject 12'sunderstanding of their sleep quality. The use of sleep qualityinformation for populations of subject (e.g., age-matched populations)may be accurate for certain subjects but may fail to accuratelyrepresent sleep quality for other subjects. The combination of abaseline model (i.e., accounting for sleep information for theage-matched population) with a personalized model (i.e., accounting forsleep information for the subject) allows for more accurate sleepquality feedback for different subjects. The use of a baseline modeladditionally removes (or at least reduces) the need for a calibrationperiod in which the system learns the habits of the subject and does notprovide sleep quality feedback. As shown in FIG. 1, system 10 includesone or more of a sensor 14, a sensory stimulator 16, external resources18, a processor 20, electronic storage 22, a subject interface 24,and/or other components. These components are further described below.

Sensor 14 is configured to generate output signals conveying informationrelated to sleep stages of subject 12 during a sleep session. The outputsignals conveying information related to sleep stages of subject 12 mayinclude information related to brain activity in subject 12. As such,sensor 14 is configured to generate output signals conveying informationrelated to brain activity. In some embodiments, sensor 14 is configuredto generate output signals conveying information related to stimulationprovided to subject 12 during sleep sessions. In some embodiments, theinformation in the output signals from sensor 14 is used to controlsensory stimulator 16 to provide sensory stimulation to subject 12 (asdescribed below).

Sensor 14 may comprise one or more sensors that generate output signalsthat convey information related to brain activity in subject 12directly. For example, sensor 14 may include electroencephalogram (EEG)electrodes configured to detect electrical activity along the scalp ofsubject 12 resulting from current flows within the brain of subject 12.Sensor 14 may comprise one or more sensors that generate output signalsconveying information related to brain activity of subject 12indirectly. For example, one or more sensors 14 may comprise a heartrate sensor that generates an output based on a heart rate of subject 12(e.g., sensor 14 may be a heart rate sensor than can be located on thechest of subject 12, and/or be configured as a bracelet on a wrist ofsubject 12, and/or be located on another limb of subject 12), movementof subject 12 (e.g., sensor 14 may comprise an accelerometer that can becarried on a wearable, such as a bracelet around the wrist and/or ankleof subject 12 such that sleep may be analyzed using actigraphy signals),respiration of subject 12, and/or other characteristics of subject 12.

In some embodiments, sensor 14 may comprise one or more of EEGelectrodes, a respiration sensor, a pressure sensor, a vital signscamera, a functional near infra-red sensor (fNIR), a temperature sensor,a microphone and/or other sensors configured to generate output signalsrelated to (e.g., the quantity, frequency, intensity, and/or othercharacteristics of) the stimulation provided to subject 12, the brainactivity of subject 12, and/or other sensors. Although sensor 14 isillustrated at a single location near subject 12, this is not intendedto be limiting. Sensor 14 may include sensors disposed in a plurality oflocations, such as for example, within (or in communication with)sensory stimulator 16, coupled (in a removable manner) with clothing ofsubject 12, worn by subject 12 (e.g., as a headband, wristband, etc.),positioned to point at subject 12 while subject 12 sleeps (e.g., acamera that conveys output signals related to movement of subject 12),coupled with a bed and/or other furniture where subject 12 is sleeping,and/or in other locations.

In FIG. 1, sensor 14, sensory stimulator 16, processor 20, electronicstorage 22, and subject interface 24 are shown as separate entities.This is not intended to be limiting. Some and/or all of the componentsof system 10 and/or other components may be grouped into one or moresingular devices. For example, these and/or other components may beincluded in a wearable device 201. In some embodiments, wearable device201 may be a headset as illustrated in FIG. 2 and/or other garments wornby subject 12. Other garments may include a cap, vest, bracelet, and/orother garment. In some embodiments, wearable device 201 may comprise oneor more sensors which may contact the skin of the subject. In someembodiments, wearable device 201 may comprise one or more sensorystimulators, which may provide visual, somatosensory, and or auditorystimulation. For example, wearable device 201 and/or other garments mayinclude, for example, sensing electrodes, a reference electrode, one ormore devices associated with an EEG, means to deliver auditorystimulation (e.g., a wired and/or wireless audio device and/or otherdevices), and one or more audio speakers. In some embodiments, wearabledevice 201 may comprise means to delivery visual, somatosensory,electric, magnetic, and/or other stimulation to the subject. In thisexample, the audio speakers may be located in and/or near the ears ofsubject 12 and/or in other locations. The reference electrode may belocated behind the ear of subject 12, and/or in other locations. In thisexample, the sensing electrodes may be configured to generate outputsignals conveying information related to brain activity of subject 12,and/or other information. The output signals may be transmitted to aprocessor (e.g., processor 20 shown in FIG. 1), a computing device(e.g., a bedside laptop) which may or may not include the processor,and/or other devices wirelessly and/or via wires. In some embodiments,the processor may be in electric communication with the one or moresensors and the one or more sensory stimulators. In some embodiments,the processor may be located within wearable device 201 and/or locatedexternally. In this example, acoustic stimulation may be delivered tosubject 12 via the wireless audio device and/or speakers. In thisexample, the sensing electrodes, the reference electrode, and the EEGdevices may be represented, for example, by sensor 14 in FIG. 1. Thewireless audio device and the speakers may be represented, for example,by sensory stimulator 16 shown in FIG. 1. In this example, a computingdevice may include processor 20, electronic storage 22, subjectinterface 24, and/or other components of system 10 shown in FIG. 1.

Stimulator 16 is configured to provide sensory stimulation to subject12. Sensory stimulator 16 is configured to provide auditory, visual,somatosensory, electric, magnetic, and/or sensory stimulation to subject12 prior to a sleep session, during a sleep session, and/or at othertimes. In some embodiments, a sleep session may comprise any period oftime when subject 12 is sleeping and/or attempting to sleep. Sleepsessions may include nights of sleep, naps, and/or other sleepssessions. For example, sensory stimulator 16 may be configured toprovide stimuli to subject 12 during a sleep session to enhance EEGsignals during deep sleep in subject 12, and/or for other purposes.

Sensory stimulator 16 is configured to affect deep sleep in subject 12through non-invasive brain stimulation and/or other methods. Sensorystimulator 16 may be configured to affect deep sleep throughnon-invasive brain stimulation using auditory, electric, magnetic,visual, somatosensory, and/or other sensory stimuli. The auditory,electric, magnetic, visual, somatosensory, and/or other sensorystimulation may include auditory stimulation, visual stimulation,somatosensory stimulation, electrical stimulation, magnetic stimulation,a combination of different types of stimulation, and/or otherstimulation. The auditory, electric, magnetic, visual, somatosensory,and/or other sensory stimuli include odors, sounds, visual stimulation,touches, tastes, somatosensory stimulation, haptic, electrical,magnetic, and/or other stimuli. The sensory stimulation may have anintensity, a timing, and/or other characteristics. For example, acoustictones may be provided to subject 12 to affect deep sleep in subject 12.The acoustic tones may include one or more series of tones of adetermined length (e.g., less than a decisecond, 50 milliseconds, etc.)separated from each other by an interval (e.g., one second). The volume(i.e., the intensity) of individual tones may be modulated based ondepth of sleep and/or other factors (as described herein). In someembodiments, the initial volume may be imperceptible, set to a defaultvolume, and/or set by the subject via a subject interface (e.g., 24, asshown in FIG. 1). The length of individual tones (e.g., the timing), theinterval between tones, the pitch of the tones, and the type of tone mayalso be adjusted. This example is not intended to be limiting, and thestimulation parameters may vary.

Examples of sensory stimulator 16 may include one or more of a soundgenerator, a speaker, a music player, a tone generator, a vibrator (suchas a piezoelectric member, for example) to deliver vibratorystimulation, a coil generating a magnetic field to directly stimulatethe brain's cortex, one or more light generators or lamps, a fragrancedispenser, and/or other devices. In some embodiments, sensory stimulator16 is configured to adjust the intensity, timing, and/or otherparameters of the stimulation provided to subject 12 (e.g., as describedbelow).

External resources 18 include sources of information (e.g., databases,websites, etc.), external entities participating with system 10 (e.g.,one or more the external sleep monitoring devices, a medical recordssystem of a health care provider, etc.), and/or other resources. In someembodiments, external resources 18 include components that facilitatecommunication of information, one or more servers outside of system 10,a network (e.g., the internet), electronic storage, equipment related toWi-Fi technology, equipment related to Bluetooth® technology, data entrydevices, sensors, scanners, computing devices associated with individualsubjects, and/or other resources. In some implementations, some or allof the functionality attributed herein to external resources 18 may beprovided by resources included in system 10. External resources 18 maybe configured to communicate with processor 20, subject interface 24,sensor 14, electronic storage 22, sensory stimulator 16, and/or othercomponents of system 10 via wired and/or wireless connections, via anetwork (e.g., a local area network and/or the internet), via cellulartechnology, via Wi-Fi technology, and/or via other resources.

Processor 20 is configured to provide information processingcapabilities in system 10. As such, processor 20 may comprise one ormore of a digital processor, an analog processor, a digital circuitdesigned to process information, an analog circuit designed to processinformation, a state machine, and/or other mechanisms for electronicallyprocessing information. Although processor 20 is shown in FIG. 1 as asingle entity, this is for illustrative purposes only. In someembodiments, processor 20 may comprise a plurality of processing units.These processing units may be physically located within the same device(e.g., sensory stimulator 16, subject interface 24, etc.), or processor20 may represent processing functionality of a plurality of devicesoperating in coordination. In some embodiments, processor 20 may beand/or be included in a computing device such as a desktop computer, alaptop computer, a smartphone, a tablet computer, a server, and/or othercomputing devices. Such computing devices may run one or more electronicapplications having graphical subject interfaces configured tofacilitate subject interaction with system 10.

As shown in FIG. 1, processor 20 is configured to execute one or morecomputer program components. The computer program components maycomprise software programs and/or algorithms coded and/or otherwiseembedded in processor 20, for example. The one or more computer programcomponents may comprise one or more of an information component 30, amodel component 32, a control component 34, a modulation component 36,and/or other components. Processor 20 may be configured to executecomponents 30, 32, 34, and/or 36 by software; hardware; firmware; somecombination of software, hardware, and/or firmware; and/or othermechanisms for configuring processing capabilities on processor 20.

It should be appreciated that although components 30, 32, 34, and 36 areillustrated in FIG. 1 as being co-located within a single processingunit, in embodiments in which processor 20 comprises multiple processingunits, one or more of components 30, 32, 34, and/or 36 may be locatedremotely from the other components. The description of the functionalityprovided by the different components 30, 32, 34, and/or 36 describedbelow is for illustrative purposes, and is not intended to be limiting,as any of components 30, 32, 34, and/or 36 may provide more or lessfunctionality than is described. For example, one or more of components30, 32, 34, and/or 36 may be eliminated, and some or all of itsfunctionality may be provided by other components 30, 32, 34, and/or 36.As another example, processor 20 may be configured to execute one ormore additional components that may perform some or all of thefunctionality attributed below to one of components 30, 32, 34, and/or36.

Information component 30 is configured to determine one or more brainactivity parameters of subject 12, and/or other information. The brainactivity parameters are determined based on the output signals fromsensor 14 and/or other information. The brain activity parametersindicate depth of sleep in subject 12. In some embodiments, theinformation in the output signals related to brain activity indicatessleep depth over time. In some embodiments, the information indicatingsleep depth over time is or includes information related to deep sleepin subject 12.

In some embodiments, the information indicating sleep depth over timemay be indicative of other sleep stages of subject 12. For example, thesleep stages of subject 12 may be associated with deep sleep, rapid eyemovement (REM) sleep, and/or other sleep. Deep sleep may be stage N3,and/or other deep sleep stages. In some embodiments, the sleep stages ofsubject 12 may be one or more of stage S1, S2, S3, or S4. In someembodiments, NREM stage 2 and/or 3 (and/or S3 and/or S4) may be slowwave (e.g., deep) sleep. In some embodiments, the information thatindicates sleep depth over time is and/or is related to one or moreadditional brain activity parameters.

In some embodiments, the information related to brain activity thatindicates sleep depth over time is and/or includes EEG informationand/or other information generated during sleep sessions of subject 12and/or at other times. In some embodiments, brain activity parametersmay be determined based on the EEG information and/or other information.In some embodiments, the brain activity parameters may be determined byinformation component 30 and/or other components of system 10. In someembodiments, the brain activity parameters may be previously determinedand be part of the historical sleep stage information obtained fromexternal resources 18 (described below). In some embodiments, the one ormore brain activity parameters are and/or are related to a frequency,amplitude, phase, presence of specific sleep patterns such as eyemovements, ponto-geniculo-occipital (PGO) wave, slow wave, and/or othercharacteristics of an EEG signal. In some embodiments, the one or morebrain activity parameters are determined based on the frequency,amplitude, and/or other characteristics of the EEG signal. In someembodiments, the determined brain activity parameters and/or thecharacteristics of the EEG may be and/or indicate sleep stages thatcorrespond to the deep sleep stage described above.

Information component 30 is configured to obtain historical sleep stageinformation. In some embodiments, the historical sleep stage informationis for subject 1 and/or other subjects. The historical sleep stageinformation is related to brain activity, and/or other physiological ofthe population of subjects and/or subject 12 that indicates sleep stagesover time during previous sleep sessions of the population of subjectsand/or subject 12. The historical sleep stage information is related tosleep stages and/or other brain parameters of the population of subjectsand/or subject 12 during corresponding sleep sessions, and/or otherinformation.

In some embodiments, information component 30 is configured to obtainthe historical sleep stage information electronically from externalresources 18, electronic storage 22, and/or other sources ofinformation. In some embodiments, obtaining the historical sleep stageinformation electronically from external resources 18, electronicstorage 22, and/or other sources of information comprises querying onemore databases and/or servers; uploading information and/or downloadinginformation, facilitating subject input, sending and/or receivingemails, sending and/or receiving text messages, and/or sending and/orreceiving other communications, and/or other obtaining operations. Insome embodiments, information component 30 is configured to aggregateinformation from various sources (e.g., one or more of the externalresources 18 described above, electronic storage 22, etc.), arrange theinformation in one or more electronic databases (e.g., electronicstorage 22, and/or other electronic databases), normalize theinformation based on one or more features of the historical sleep stageinformation (e.g., duration of sleep sessions, number of sleep sessions,number of sleep disruptions, duration of various sleep stages, and/orsleep time regularity etc.) and/or perform other operations.

Model component 32 is configured such that a trained deep learningalgorithm and/or any other supervised machine deep learning algorithmsare caused to detect deep sleep in subject 12. In some embodiments, thismay be and/or include determining periods when subject 12 isexperiencing deep sleep during the sleep session and/or otheroperations. The determined deep sleep, and/or timing, indicates whethersubject 12 is in deep sleep for stimulation and/or other information. Byway of a non-limiting example, a trained deep learning algorithm may becaused to determine deep sleep stages and/or timing of the deep sleepstages for the subject based on the output signals (e.g., using theinformation in the output signals as input for the model) and/or otherinformation. In some embodiments, model component 32 is configured toprovide the information in the output signals to the deep learningalgorithm in temporal sets that correspond to individual periods duringthe sleep session. In some embodiments, model component 32 is configuredto cause the trained deep learning algorithm to output the determinedsleep stages of deep sleep for subject 12 during the sleep session basedon the temporal sets of information. (The functionality of modelcomponent 32 is further discussed below relative to FIG. 2-3.)

As an example, deep learning algorithms may be a deep neural network. Adeep neural network may be based on a large collection of neural units(or artificial neurons). Deep learning algorithms may loosely mimic themanner in which a biological brain works (e.g., via large clusters ofbiological neurons connected by axons). Each neural unit of a deeplearning algorithm may be connected with many other neural units of thedeep learning algorithm. Such connections can be enforcing or inhibitoryin their effect on the activation state of connected neural units. Insome embodiments, each individual neural unit may have a summationfunction that combines the values of all its inputs together. In someembodiments, each connection (or the neural unit itself) may have athreshold function such that a signal must surpass the threshold beforeit is allowed to propagate to other neural units. These deep learningalgorithm systems may be self-learning and trained, rather thanexplicitly programmed, and can perform significantly better in certainareas of problem solving, as compared to traditional computer programs.In some embodiments, deep learning algorithms may include multiplelayers (e.g., where a signal path traverses from front layers to backlayers). In some embodiments, back propagation techniques may beutilized by the deep learning algorithms, where forward stimulation isused to reset weights on the “front” neural units. In some embodiments,stimulation and inhibition for deep learning algorithms may be more freeflowing, with connections interacting in a more chaotic and complexfashion.

As described above, a trained deep neural network may comprise one ormore intermediate or hidden layers. The intermediate layers of thetrained deep neural network include one or more convolutional layers,one or more recurrent layers, and/or other layers of the trained deeplearning algorithm. Individual intermediate layers receive informationfrom another layer as input and generate corresponding outputs. Thedetected sleep stages of deep sleep are generated based on theinformation in the output signals from sensor 14 as processed by thelayers of the deep learning algorithm.

Control component 34 is configured to control stimulator 16 to providestimulation to subject 12 during sleep and/or at other times. Controlcomponent 34 is configured to cause sensory stimulator 16 to provide thesensory stimulation to subject 12 during deep sleep to affect deep sleepin subject 12 during a sleep session. Control component 34 is configuredto cause sensory stimulator 16 to provide sensory stimulation to subject12 based on a detected deep sleep stage (e.g., the output from modelcomponent 32) and/or other information. Control component 34 isconfigured to cause sensory stimulator 16 to provide the sensorystimulation to subject 12 based on the detected deep sleep stage and/orother information over time during the sleep session. Control component34 is configured to cause sensory stimulator 16 to provide sensorystimulation to subject 12 responsive to subject 12 being in, or likelybeing in, deep sleep for stimulation. For example, control component 34is configured such that controlling one or more sensory stimulators 16to provide the sensory stimulation to subject 12 during the deep sleepto affect the deep sleep in subject 12 during the sleep sessioncomprises: determining the periods when subject 12 is experiencing deepsleep, causing one or more sensory stimulators 16 to provide the sensorystimulation to subject 12 during the periods when subject 12 isexperiencing deep sleep, and/or causing one or more sensory stimulators16 to modulate (e.g., as described herein), an amount, a timing, and/orintensity of the sensory stimulation provided to subject 12 based on theone or more values of the one or more intermediate layers. In someembodiments, stimulators 16 are controlled by control component 34 toaffect deep sleep through (e.g., peripheral auditory, magnetic,electrical, and/or other) stimulation delivered during deep sleep (asdescribed herein).

In some embodiments, control component 34 is configured to controlsensory stimulator 16 to deliver sensory stimulation to subject 12responsive to model component 32 determining that subject 12 hasremained in deep sleep for a continuous threshold amount of time duringthe sleep session. For example, model component 32 and/or controlcomponent 34 may be configured such that on detection of deep sleep,model component 32 starts a (physical or virtual) timer configured totrack the time subject 12 spends in deep sleep. Control component 34 isconfigured to deliver auditory stimulation responsive to the durationthat subject 12 spends in continuous deep sleep breaching a predefinedduration threshold. In some embodiments, the predefined durationthreshold is determined at manufacture of system 10 and/or at othertimes. In some embodiments, the predefined duration threshold isdetermined based on information from previous sleep sessions of subject12 and/or subjects demographically similar to subject 12 (e.g., asdescribed above). In some embodiments, the predefined duration thresholdis adjustable via subject interface 24 and/or other adjustmentmechanisms.

In some embodiments, the predefined deep sleep duration threshold may beone minute and/or other durations, for example. By way of a non-limitingexample, control component 34 may be configured such that auditorystimulation starts once a minute of continuous deep sleep in subject 12is detected. In some embodiments, once the stimulation begins, controlcomponent 34 is configured to control stimulation parameters of thestimulation. Upon detection of a sleep stage transition (e.g., from deepsleep to some other sleep stage), control component 34 is configured tostop stimulation. Modulation component 36 is configured to cause sensorystimulator 16 to modulate an amount, a timing, and/or intensity of thesensory stimulation. Modulation component 36 is configured to causesensory stimulator 16 to modulate the amount, timing, and/or intensityof the sensory stimulation based on the brain activity parameters,values output from the intermediate layers of the trained deep learningalgorithm, and/or other information. As an example, sensory stimulator16 is caused to modulate the timing and/or intensity of the sensorystimulation based on the brain activity parameters, the values outputfrom the convolutional layers, the values output from the recurrentlayers, and/or other information. For example, modulation component 36may be configured such that sensory stimulation is delivered with anintensity that is proportional to a predicted probability value (e.g.,an output from an intermediate layer of a deep learning algorithm) of aparticular sleep stage (e.g., deep sleep). In this example, the higherthe probability of deep sleep, the more likely the stimulationcontinues. If sleep micro-arousals are detected and the sleep stageremains in deep sleep, modulation component 36 may be configured suchthat the intensity of the stimulation is decreased (by for instance fivedBs) responsive to individual micro-arousal detections.

By way of a non-limiting example, FIG. 2 illustrates several of theoperations performed by system 10 and described above. In the exampleshown in system 200 of FIG. 2, an EEG signal 202 is processed and/orotherwise provided (e.g., by information component 30 and modelcomponent 32 shown in FIG. 1) to a deep learning algorithm 206 intemporal windows 204. Deep learning algorithm 206 detects sleep stages(e.g., N3, N2, N1, REM, and wakefulness). Determination 210 indicateswhether the subject is in deep (N3) sleep. Deep learning algorithm 206may determine the sleep stage of the subject using methods described inthe publication “Recurrent Deep Neural Networks for Real-Time SleepStage Classification From Single Channel EEG.” Frontiers inComputational Neuroscience. Bresch, E., Großekathöfer, U., andGarcia-Molina, G. (2018), which is hereby incorporated by reference inits entirety.

As shown in FIG. 2, deep learning algorithm 206 outputs soft predictionprobabilities 208. Soft prediction probabilities 208 are predictionprobabilities for individual sleep stages. The set of soft predictionprobabilities 208 constitute a so-called soft decision vector, which maybe translated into a hard decision by determining which sleep stage isassociated with a highest probability value (in a continuum of possiblevalues) relative to other sleep stages. These soft decisions make itpossible for system 10 to consider different possible sleep states on acontinuum rather than being forced to decide which discrete sleep stage“bucket” particular EEG information fits into (as in prior art systems).The terms “soft” and “hard” are not intended to be limiting but may behelpful to use to describe the operations performed by the system. Forexample, the term “soft output” may be used, because at this stage, anydecision is possible. Indeed, the final decision could depend onpost-processing of the soft outputs, for example.

Determination 210 indicates whether deep sleep is detected. If deepsleep is not detected at determination 210, system 200 returns toprocessing EEG signal 202 in temporal window 204 by deep learningalgorithm 206. If deep sleep is detected at determination 210, the oneor more sensory stimulators apply sensory stimulation 212 to thesubject. As described above, the sensory stimulation may be repeatingstimulations (e.g., vibrations, light pulses, etc.) and/or constantstimulations. Repeating stimulations may be separated from one anotherbe a constant interval, and the intensity (i.e., volume, brightness,etc.) of the stimulations may vary based on the depth of sleep. Theseparameters (e.g., volume and timing 216 and/or other parameters) arecalculated based on features 214 of the EEG. For example, features 214of the EEG may indicate that the subject has been in deep sleep for athreshold period of time and that the likelihood of microarousals islow. The one or more sensory stimulators may then increase the volume asthe depth of sleep increases (e.g., increased slow wave activity). Insome embodiments, the increase in volume may be proportional to thedepth of sleep or may otherwise correspond to the depth of sleep.

FIG. 3 illustrates slow wave activity enhancement for a subject during asleep session. Example 300 shows the effect of sensory stimulations 304on slow wave activity 306 of the subject. Slow wave activity 306 ingraph 302 has a relatively constant amplitude until time zero, whensensory stimulation 304 is applied. Once sensory stimulation 304 isapplied, the amplitude of the EEG signal for slow wave activity 306increases. In example 300, ten stimulations are applied with a constantone-second interval separating the stimulations. In some embodiments,the stimulations may be 50 milliseconds in length and may have anintensity (e.g., volume, brightness, etc.) that is adjusted based on thedepth of sleep. The duration, timing, intensity, and other factors arenot limited to example 300 and may vary. In addition, example 300depicts ten stimulations followed by a period of no stimulations. Insome embodiments, the sensory stimulators may cease providing sensorystimulation if the subject exits deep sleep and/or if micro-arousals aredetected. In some embodiments, if the subject remains in deep sleep andno micro-arousals are detected, the repeating and/or constantstimulations may continue to be delivered to the subject.

Example 350 shows slow wave activity in a subject during a sleep sessionwith sensory stimulation as compared to slow wave activity in thesubject during a sleep session without sensory stimulation. In graph308, stimulated slow wave activity 310 is enhanced at the tone locations314, i.e., the largest increases in slow wave activity occur at tonelocations 314. Additionally, unstimulated slow wave activity 312 isoverall lower than stimulated slow wave activity 310. Graph 316 showscumulative slow wave activity information for the subject across thesame range of times as graph 308. Tone locations 322 align temporallywith tone locations 314. As shown in graph 316, cumulative stimulatedslow wave activity 318 increases at tone locations 322. Cumulativestimulated slow wave activity 318 is overall greater than cumulativeunstimulated slow wave activity 320.

Returning to FIG. 1, model component 32 is configured such that both thevalues output from convolutional layers, and the soft decision valueoutputs, are vectors comprising continuous values as opposed to discretevalues such as sleep stages. Consequently, convolutional and recurrent(soft decision) value outputs are available to be used by system 10 tomodulate the volume of the stimulation when the deep learning algorithmdetects occurrences of deep sleep. In addition, as described herein,parameters determined (e.g., by information component 30 shown inFIG. 1) based on the raw sensor output signals (e.g., EEG signals) canbe used to modulate stimulation settings.

As described above, modulation component 36 is configured to causesensory stimulator 16 to modulate an amount, timing, and/or intensity ofthe sensory stimulation. Modulation component 36 is configured to causesensory stimulator to modulate the amount, timing, and/or intensity ofthe sensory stimulation based on the one or more brain activity and/orother parameters, values output from the convolutional and/or recurrentlayers of the trained deep learning algorithm, and/or other information.As an example, the interval of auditory stimulation provided to subject12 may be adjusted and/or otherwise controlled (e.g., modulated) basedon value outputs from the deep learning algorithm such as convolutionallayer value outputs and recurrent layer value outputs (e.g., sleep stage(soft) prediction probabilities). In some embodiments, modulationcomponent 36 is configured to cause one or more sensory stimulators 16to modulate the amount, timing, and/or intensity of the sensorystimulation, wherein the modulation comprises adjusting the interval,the stimulation intensity, and/or the stimulation frequency, responsiveto an indication subject 12 is experiencing one or more micro-arousals.

In some embodiments, modulation component 36 is configured to modulatethe sensory stimulation based on the brain activity and/or otherparameters alone, which may be determined based on the output signalsfrom sensors 14 (e.g., based on a raw EEG signal). In these embodiments,the output of a deep learning algorithm (and/or other machine learningmodels) continues to be used to detect sleep stages (e.g., as describedabove). However, the stimulation intensity and timing are insteadmodulated based on brain activity and/or other parameters or propertiesdetermined based on the sensor output signals. In some embodiments, theinformation in, or determined based on, the sensor output signals canalso be combined with intermediate outputs of the network such as outputof the convolution layers or the final outputs (soft stages) to modulateintensity and timing (e.g., as described herein).

FIG. 4 illustrates contributing factors for sleep quality feedback.Quantitative sleep quality feedback is useful the subject (e.g., 12, asshown in FIG. 1) to assess the quality of their sleep and factors whichdetract from and/or improve sleep quality. Sleep quality feedback may beprovided as a part of a sleep enhancement system (e.g., such as theSmartSleep system) and/or separately. In some embodiments, thecontributing factors may include sleep architecture factors andcumulative slow wave activity throughout the sleep session. The factorsare combined to produce a sleep quality score for the sleep session.

System 400 illustrates one method of combining relevant factors tocalculate a sleep quality score. System 400 may begin with score 402.Score 402 may be an initial score (e.g., 100%) which represents aperfect score. In some embodiments, score 402 represents the sleepquality of a perfect night of sleep (e.g., having a sufficiently longduration, with no disruptions, etc.). Other inputs include subjecthistory database 408 and reference database 410. Subject historydatabase 408 may provide information such as regular bedtimes and wakeuptimes, as well as typical slow wave activity of the subject (e.g., 12,as shown in FIG. 1) across sleep sessions. Reference database 410 maystore and/or provide information related to matched populations (e.g.,such as gender-matched populations, age-matched populations, and/orother matched populations). In some embodiments, the information relatedto increases in slow wave activity in the age matched population ofsubjects is received by reference database 410 and/or is preprogrammedwithin reference database 410. Stored information related to the matchedpopulations may include typical sleep architecture information for thematched populations (e.g., bedtimes and wakeup times, slow wave activityof the populations across sleep sessions, and/or other information). Inaddition, sleep architecture metrics 406 for the subject are alsofactored into the sleep feedback calculation. Sleep architecture metricsmay include information about the slow wave activity, cumulative slowwave activity, duration, disruptions, and regularity of the subject'ssleep session and/or sessions. In some embodiments, sleep architecturemetrics 406 may be based upon EEG signals measured by one or moresensors (e.g., 14, as shown in FIG. 1).

In some embodiments, system 400 may use the information received fromsleep architecture metrics 406, subject history database 408, andreference database 410 to determine deductions 404. In some embodiments,deductions 404 are subtracted from sleep score 402. Deductions 404 maybe features identified from sleep architecture metrics 406, subjecthistory database 408, and reference database 410 which have a negativeimpact on sleep quality. Deductions 404 may include total sleepduration, wake after sleep onset, sleep onset latency, number of sleepdisruptions, deep sleep duration, REM sleep duration, bedtimeregularity, wakeup time regularity, and/or other factors. In someembodiments, each factor has a pre-defined value and/or range of valuesthat is subtracted from sleep score 402 if identified within thesubject's sleep session and/or sessions. In some embodiments, thesubject may input values and/or ranges of values for deductions 404 viaa subject interface (e.g., 24, as shown in FIG. 1).

In some embodiments, once deductions 404 have been subtracted from score402, the resulting score is combined with a boost calculation 412. Theboost calculation 412 is representative of the improvement to sleepquality of the sleep session and/or sessions that resulted from thesensory stimulations provided to the subject (e.g., 12, as shown in FIG.1). For example, boost calculation 412 may represent increases incumulative slow wave activity in the subject during the sleep sessionand/or sessions as a result of the stimulation provided to the subjectvia sensory stimulators (e.g., 16, as shown in FIG. 1). In someembodiments, boost calculation 412 may be based upon a matchedpopulation (e.g., age-matched, gender-matched, BMI-matched, and/or othermatched population). In some embodiments, boost calculation 412 may bebased upon slow wave activity information that is specific to thesubject. In some embodiments, boost calculation 412 may be based uponanother source of sleep enhancement information. In some embodiments,boost calculation 412 may be based upon any combination of theaforementioned sources. In some embodiments, the one or more processors(e.g., 20, as shown in FIG. 1), may convert the slow wave activityinformation into a boost score (e.g., compatible with score 402 anddeductions 404). The one or more processors may then combine boostcalculation 412 with the combination of score 402 and deductions 404.The resulting score, sleep quality feedback score 418, representsnegative effects on the subject's sleep session and/or sessions as wellas the positive effects of the sensory stimulation (e.g., via sensorystimulators 16).

Boost calculation 412 may be calculated using various techniques. Thesetechniques may alter the accuracy of boost calculation 412 and resultingsleep quality feedback score 418. A technique which combines severalsources of sleep quality information, as described in FIG. 5, mayprovide increased accuracy of sleep quality feedback scores for certainsubjects.

FIG. 5 illustrates components of a sleep boost calculation (e.g., boostcalculation 412, as shown in FIG. 4). System 500 shows the combinationof a baseline model 504 (i.e., matched population sleep information) anda personalized model 510 (i.e., specific to the subject). In someembodiments, baseline model 504 may comprise information about increasesin the slow wave activity throughout deep sleep for different age rangesas a function of sensory stimulation provided to the age matchedpopulation of subjects. In some embodiments, the information related toincreases in slow wave activity in the age matched population ofsubjects is received or preprogrammed. The system may select the agegroup that corresponds to the subject (e.g., 12, as shown in FIG. 1) andmay use the associated slow wave activity information for the age groupas an approximation of the slow wave activity of the subject during thesleep session. An example of using data from an age matched populationof subject to determine an increase in slow wave activity in a subjectis described in European Pat. App. Pub. No. 3457411, which is herebyincorporated by reference in its entirety. Baseline model 504 mayutilize inputs 502, which may include cumulative slow wave activityinformation for a sleep session for the selected age group, a number ofstimulations delivered to the subject during the sleep session and/or aduration of stimulations delivered to the subject during the sleepsession, deep sleep information (e.g., depth of sleep, duration of deepsleep, and/or other deep sleep factors), and/or additional inputs. Theone or more processors (e.g., 20, as shown in FIG. 1), may combineinputs 502 to produce a slow wave activity enhancement value for thesleep session according to the baseline model. This slow wave activityenhancement value may be baseline boost 506. In some embodiments,baseline boost 506 may be used as the value for boost calculation 412.In some embodiments, baseline boost 506 may be combined with a slow waveactivity enhancement value for the sleep session according to thepersonalized model in order to calculate boost calculation 412.

In some embodiments, a threshold amount of sleep data for the subject(e.g., 12, as shown in FIG. 1) is needed before the system can createpersonalized model 512. Therefore, system 500 may use initial model 518to collect and analyze the subject's initial sleep data. For example,initial model 518 may utilize inputs 516 which include data for a numberof sleep sessions. In some embodiments, the number of sleep sessionscomprising the data for inputs 516 may be the first several sleepsessions (e.g., the first seven, ten, or another number of sleepsessions) for which the subject is receiving sensory stimulation. Insome embodiments, the number of initial sleep sessions may vary. In someembodiments, only valid sleep sessions are included in the initial sleepsessions. In some embodiments, if a pre-determined number of valid sleepsessions is exceeded, only the most recent valid sleep sessions areincluded. In some embodiments, data collected from the initial sleepsessions may comprise cumulative slow wave activity information for theinitial sleep sessions and the duration and/or number of stimulationsprovided to the subject during the initial sleep sessions. This data maybe included in inputs 516. Baseline boost 506 may also be included ininputs 516. In some embodiments, initial model 518 may collect inputs516 until a threshold number of initial sleep sessions is reached, atwhich time, initial model 518 may generate personalized model 512.

In some embodiments, personalized model 512 may initially utilize thedata collected by initial model 518. The data collected by initial model518 may comprise baseline boost 506, cumulative slow wave activity,stimulation information for the initial sleep sessions, and/or otherinformation (i.e., inputs 516). Personalized model 512 may combine thisdata to calculate personalized boost 514. In some embodiments,personalized model 512 may use various equations to combine inputs 516to generate personalized boost 514. In some embodiments, personalizedmodel 512 may use one or more of equations 1 and 2 and/or otherequations.

$\begin{matrix}{{{Personalized}\mspace{14mu} {Boost}} = {\beta_{0} + {\beta_{1} \times {Tones}} + {\beta_{2} \times {CSW}\mspace{14mu} {A:}}}} & {{Equation}\mspace{14mu} 1} \\{\mspace{79mu} {{\beta_{2} = {{\frac{\begin{matrix}{{{\langle{Tones}^{2}\rangle} \times {\langle{{CSW}\mspace{11mu} A \times {Boost}}\rangle}} -} \\{{\langle{{Tones} \times {CSW}\mspace{11mu} A}\rangle}{\langle{{Tones} \times {Boost}}\rangle}}\end{matrix}}{{{\langle{Tones}^{2}\rangle}{\langle{{CSW}\mspace{11mu} A^{2}}\rangle}} - {\langle{{Tones} \times {CSW}\mspace{11mu} A}\rangle}^{2}}:\mspace{20mu} \beta_{1}} = \frac{\begin{matrix}{{{\langle{{CSW}\mspace{11mu} A^{2}}\rangle} \times {\langle{{CSW}\mspace{11mu} A \times {Boost}}\rangle}} -} \\{{\langle{{Tones} \times {CSW}\mspace{11mu} A}\rangle}{\langle{{CSW}\mspace{11mu} A \times {Boost}}\rangle}}\end{matrix}}{{{\langle{Tones}^{2}\rangle}{\langle{{CSW}\mspace{11mu} A^{2}}\rangle}} - {\langle{{Tones} \times {CSW}\mspace{11mu} A}\rangle}^{2}}}}\mspace{20mu} {\beta_{0} = {{\langle{Boost}\rangle} - {\beta_{1} \times {\langle{Tones}\rangle}} - {\beta_{2} \times {\langle{{CSW}\mspace{11mu} A}\rangle}}}}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$where:

${\langle{Tones}\rangle} = {\frac{1}{n}{\sum_{i}^{n}{Tones}_{i}}}$${\langle{{CSW}\mspace{11mu} A}\rangle} = {\frac{1}{n}{\sum_{i}^{n}{{CSW}\mspace{11mu} A_{i}}}}$${\langle{Boost}\rangle} = {\frac{1}{n}{\sum_{i}^{n}{Boost}_{i}}}$${\langle{{Tones} \times {Boost}}\rangle} = {\frac{1}{n}{\sum_{i = 1}^{n = 7}{{Tones}_{i} \times {Boost}_{i}}}}$${\langle{{Tones} \times {CSWA}}\rangle} = {\frac{1}{n}{\sum_{i = 1}^{n = 7}{{Tones}_{i} \times {CSW}\mspace{11mu} A_{i}}}}$${\langle{{CSW}\mspace{11mu} A \times {Boost}}\rangle} = {\frac{1}{n}{\sum_{i = 1}^{n = 7}{{CSW}\mspace{11mu} A_{i} \times {Boost}_{i}}}}$${\langle{Tones}^{2}\rangle} = {\frac{1}{n}{\sum_{i}^{n}{{Tones}_{i} \times {Tones}_{i}}}}$${\langle{{CSW}\mspace{11mu} A^{2}}\rangle} = {\frac{1}{n}{\sum_{i}^{n}{{CSW}\mspace{11mu} A_{i} \times {CSW}\mspace{11mu} A_{i}}}}$

In some embodiments, n may be a number of initial sleep sessions forwhich initial model 518 must receive data before generating personalizedmodel 512. In some embodiments, for calculations of the initial sleepsessions, the boost values in equations 1 and 2 are baseline boostvalues (i.e., baseline boost 506), which relate to the matchedpopulation.

As shown in system 500, personalized boost 514 may be combined withbaseline boost 506 to generate boost 508. In some embodiments, system500 may use equation 3 and/or other equations to calculate boost 508.

Boost=(1−λ)×baseline boost+λ×personalized boost  Equation 3:

In some embodiments, lambda may be a constant between zero and one. Insome embodiments, lambda controls the relative importance of baselineboost and personalized boost to overall boost. In some embodiments,lambda may be a variable. For example, the system may initially use asmall value of lambda (e.g., 0.1) in order to increase the relativeimportance of baseline boost for an initial number of sleep sessions.Thereafter, the system may increase the value of lambda such that thepersonalized boost plays an increasingly important role in calculatingoverall boost. In some embodiments, boost 508 may be used for boostcalculation 412, as shown in FIG. 4.

In some embodiments, coefficients β₀, β₁, and β₂ may be empiricallyestimated with available data and may be continuously updated as newdata becomes available. In some embodiments, system 500 may use equation4 and/or other equations to continuously update the calculations above.

Personalized Boost_(n+1)=β₀+β₁×Tones_(n+1)

Boost_(n+1)=(1−λ)×baseline boost_(n+1)+λ×personalizedboost_(n+1)  Equation 4:

Once data is available for a subsequent sleep session (i.e., each sleepsession after the initial sleep sessions), system 500 must updateequation 2. In some embodiments, system 500 may use equation 5 and/orother equations to update equation 2.

Tones

=(1−θ)×

Tones

+θ×Tones_(n+1)

CSWA

=(1−θ)×

CSWA

+θ×CSWA_(n+1)

Boost

=(1−θ)×

Boost

+θ×Boost_(n+1)

Tones×Boost

=(1−θ)×

Tones×Boost

+θ×Tones_(n+1)×Boost_(n+1)

CSWA×Boost

=(1−θ)×

CSWA×Boost

+θ×CSWA_(n+1)×Boost_(n+1)

Tones×CSWA

=(1−θ)×

Tones×CSWA

+θ×Tones_(n+1)×CSWA_(n+1)

Tones²

=(1−θ)×

Tones²

+θ×Tones_(n+1)×Tones_(n+1)

CSWA²

=(1−θ)×CSWA+θ×CSWA_(n+1)×CSWA_(n+1)  Equation 5:

In some embodiments, θ is a constant between zero and one which controlsthe influence of the new data on the updating of personalized model 512.For example, a higher value of θ leads to a higher influence of theupdating of personalized model 512. In some embodiments, a default valueof θ may be 0.1 (e.g., if there are approximately ten valid sleepsessions factored into equation 2). In some embodiments, the value of θand/or the number of valid sleep sessions factored into equation 2 mayvary.

Returning to FIG. 4, in some embodiments, for sleep sessions fallingwithin the initial sleep sessions, any sleep quality feedback deliveredto the subject may utilize baseline boost 506 for boost calculation 412.In some embodiments, for sleep sessions that occur after the initialsleep sessions, system 400 may utilize boost 508, which includes boostinformation for the matched population as well as information specificto the subject, for boost calculation 412.

In some embodiments, the sleep quality feedback score 418 is calculatedfor each sleep session of the subject. In some embodiments, sleepquality feedback score 418 is calculated for several sleep sessions ofthe subject. In some embodiments, sleep quality feedback score 418represents the subject's sleep quality over time and may be updated withinformation from each new sleep session. In some embodiments, sleepquality feedback score 418 is provided to the subject after each sleepsession. Sleep quality feedback score 418 may be provided to the subjectvia the same device that is used to provide sensory stimulation to thesubject (e.g., headset 201, as shown in FIG. 2). In some embodiments,sleep quality feedback score 418 is provided to the subject via aseparate application (e.g., on a mobile phone, tablet, computer, etc.).In some embodiments, sleep quality feedback score 418 may be deliveredto the subject as a message (e.g., via text message or email). Sleepquality feedback score 418 may be provided to the subject using anycombination of the aforementioned methods and/or other methods.

In some embodiments, the one or more processors (e.g., 20, as shown inFIG. 1) may be configured to control the one or more sensory stimulators(e.g., 16, as shown in FIG. 1) based on baseline model 504 and updatedpersonalized model 512. For example, if sleep feedback score 418indicates poor sleep quality, the one or more processors may adjuststimulation parameters (e.g., by increasing the intensity) for thesensory stimulation. The sensory stimulators may then deliver thesensory stimulation to the subject (e.g., in a subsequent sleep session)according to the adjusted stimulation parameters.

Returning to FIG. 1, electronic storage 22 comprises electronic storagemedia that electronically stores information. The electronic storagemedia of electronic storage 22 may comprise one or both of systemstorage that is provided integrally (i.e., substantially non-removable)with system 10 and/or removable storage that is removably connectable tosystem 10 via, for example, a port (e.g., a USB port, a firewire port,etc.) or a drive (e.g., a disk drive, etc.). Electronic storage 22 maycomprise one or more of optically readable storage media (e.g., opticaldisks, etc.), magnetically readable storage media (e.g., magnetic tape,magnetic hard drive, floppy drive, etc.), electrical charge-basedstorage media (e.g., EPROM, RAM, etc.), solid-state storage media (e.g.,flash drive, etc.), cloud storage, and/or other electronically readablestorage media. Electronic storage 22 may store software algorithms,information determined by processor 20, information received via subjectinterface 24 and/or external computing systems (e.g., external resources18), and/or other information that enables system 10 to function asdescribed herein. Electronic storage 22 may be (in whole or in part) aseparate component within system 10, or electronic storage 22 may beprovided (in whole or in part) integrally with one or more othercomponents of system 10 (e.g., processor 20).

Subject interface 24 is configured to provide an interface betweensystem 10 and subject 12, and/or other subjects through which subject 12and/or other subjects may provide information to and receive informationfrom system 10. This enables data, cues, results, and/or instructionsand any other communicable items, collectively referred to as“information,” to be communicated between a subject (e.g., subject 12)and one or more of sensor 14, sensory stimulator 16, external resources18, processor 20, and/or other components of system 10. For example, ahypnogram, EEG data, deep sleep stage probability, and/or otherinformation may be displayed for subject 12 or other subjects viasubject interface 24. As another example, subject interface 24 may beand/or be included in a computing device such as a desktop computer, alaptop computer, a smartphone, a tablet computer, and/or other computingdevices. Such computing devices may run one or more electronicapplications having graphical subject interfaces configured to provideinformation to and/or receive information from subjects.

Examples of interface devices suitable for inclusion in subjectinterface 24 comprise a keypad, buttons, switches, a keyboard, knobs,levers, a display screen, a touch screen, speakers, a microphone, anindicator light, an audible alarm, a printer, a tactile feedback device,and/or other interface devices. In some embodiments, subject interface24 comprises a plurality of separate interfaces. In some embodiments,subject interface 24 comprises at least one interface that is providedintegrally with processor 20 and/or other components of system 10. Insome embodiments, subject interface 24 is configured to communicatewirelessly with processor 20 and/or other components of system 10.

It is to be understood that other communication techniques, eitherhard-wired or wireless, are also contemplated by the present disclosureas subject interface 24. For example, the present disclosurecontemplates that subject interface 24 may be integrated with aremovable storage interface provided by electronic storage 22. In thisexample, information may be loaded into system 10 from removable storage(e.g., a smart card, a flash drive, a removable disk, etc.) that enablesthe subject(s) to customize the implementation of system 10. Otherexemplary input devices and techniques adapted for use with system 10 assubject interface 24 comprise, but are not limited to, an RS-232 port,RF link, an IR link, modem (telephone, cable or other). In short, anytechnique for communicating information with system 10 is contemplatedby the present disclosure as subject interface 24.

FIG. 6 illustrates method 600 for measuring slow wave activity of asubject during a sleep session. The system comprises one or moresensors, one or more sensory stimulators, one or more processorsconfigured by machine-readable instructions, and/or other components.The one or more processors are configured to execute computer programcomponents. The computer program components comprise an informationcomponent, a model component, a control component, a modulationcomponent, and/or other components. The operations of method 600presented below are intended to be illustrative. In some embodiments,method 600 may be accomplished with one or more additional operationsnot described, and/or without one or more of the operations discussed.Additionally, the order in which the operations of method 600 areillustrated in FIG. 6 and described below is not intended to belimiting.

In some embodiments, method 600 may be implemented in one or moreprocessing devices such as one or more processors 20 described herein(e.g., a digital processor, an analog processor, a digital circuitdesigned to process information, an analog circuit designed to processinformation, a state machine, and/or other mechanisms for electronicallyprocessing information). The one or more processing devices may includeone or more devices executing some or all of the operations of method600 in response to instructions stored electronically on an electronicstorage medium. The one or more processing devices may include one ormore devices configured through hardware, firmware, and/or software tobe specifically designed for execution of one or more of the operationsof method 600.

At an operation 602, output signals conveying information related tobrain activity of the subject during the sleep session are generated.The output signals are generated during a sleep session of the subjectand/or at other times. In some embodiments, operation 602 is performedby sensors the same as or similar to sensors 14 (shown in FIG. 1 anddescribed herein).

In some embodiments, operation 602 includes providing the information inthe output signals to the deep learning algorithm in temporal sets thatcorrespond to individual periods of time during the sleep session. Insome embodiments, operation 602 includes causing the trained deeplearning algorithm to output the detected deep sleep for the subjectduring the sleep session based on the temporal sets of information. Insome embodiments, operation 602 is performed by a processor componentthe same as or similar to model component 32 (shown in FIG. 1 anddescribed herein).

At an operation 604, slow wave activity is detected in the subjectduring a sleep session based on the output signals. In some embodiments,the slow wave activity indicates a sleep stage (e.g., N3, N2, N1, REM,or wakefulness). If the slow wave activity of the subject indicates deepsleep (e.g., N3 sleep stage), the one or more processors may control theone or more sensory stimulators to provide sensory stimulation to thesubject during the deep sleep. In some embodiments, the one or moreprocessors may determine that the subject has been in deep sleep for athreshold amount of time before controlling the sensory stimulators toprovide sensory stimulation. In some embodiments, the one or moreprocessors may determine that the likelihood of microarousals is below athreshold before controlling the sensory stimulators to provide sensorystimulation. In some embodiments, operation 604 is performed by aprocessor component the same as or similar to control component 34(shown in FIG. 1 and described herein).

At an operation 606, sensory stimulation is delivered to the subject toincrease the slow wave activity. In some embodiments, the sensorystimulation may be in the form of auditory vibrations, hapticvibrations, light pulses, and/or another type of sensory stimulation. Insome embodiments, the sensory stimulation may be provided to the subjectas repeating stimulations with a constant interval between stimulationsand/or constant stimulations. In some embodiments, the sensorystimulators may vary the intensity of the stimulations based on thedepth of sleep (e.g., as detected by the one or more sensors). In someembodiments, the parameters (e.g., amount, timing, intensity, etc.) maybe modulated by the sensory stimulators (e.g., 16, as shown in FIG. 1).In some embodiments, operation 606 is performed by a processor componentthe same as or similar to modulation component 36 (shown in FIG. 1 anddescribed herein).

At an operation 608, baseline information is obtained via a baselinemodel, wherein the baseline information is related to a baseline slowwave activity increase derived from sensory stimulation provided to anage matched population of subject. The baseline information may compriseinformation about increases in the slow wave activity during deep sleepfor different age ranges as a function of sensory stimulation providedto the age matched population of subjects. In some embodiments, theinformation related to increases in slow wave activity in the agematched population of subjects is received or preprogrammed. The systemmay select the age group that corresponds to the subject. In someembodiments, operation 608 is performed by a processor component thesame as or similar to control component 34 (shown in FIG. 1 anddescribed herein).

At an operation 610, a personalized model is generated, where thepersonalized model is based on the baseline information obtained via thebaseline model, an amount of sensory stimulation delivered to thesubject, and an amount of slow wave activity of the subject during priorsleep sessions in which sensory stimulation was provided to the subject.In some embodiments, the personalized model is configured to providepersonalized information related to a slow wave activity increasederived from the sensory stimulation provided to the subject. In someembodiments, operation 610 is performed by a processor component thesame as or similar to control component 34 (shown in FIG. 1 anddescribed herein.

In the claims, any reference signs placed between parentheses shall notbe construed as limiting the claim. The word “comprising” or “including”does not exclude the presence of elements or steps other than thoselisted in a claim. In a device claim enumerating several means, severalof these means may be embodied by one and the same item of hardware. Theword “a” or “an” preceding an element does not exclude the presence of aplurality of such elements. In any device claim enumerating severalmeans, several of these means may be embodied by one and the same itemof hardware. The mere fact that certain elements are recited in mutuallydifferent dependent claims does not indicate that these elements cannotbe used in combination.

Although the description provided above provides detail for the purposeof illustration based on what is currently considered to be the mostpractical and preferred embodiments, it is to be understood that suchdetail is solely for that purpose and that the disclosure is not limitedto the expressly disclosed embodiments, but, on the contrary, isintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the appended claims. For example, it isto be understood that the present disclosure contemplates that, to theextent possible, one or more features of any embodiment can be combinedwith one or more features of any other embodiment.

What is claimed is:
 1. A system configured to measure slow wave activityduring a sleep session, the system comprising: one or moreelectroencephalogram sensors; one or more sensory stimulators configuredto provide first auditory tones to a subject; and one or more processorscoupled to the one or more sensors and the one or more sensorystimulators, the one or more processors configured by machine-readableinstructions to: detect, via the electroencephalogram sensors, slow waveactivity of the subject during a sleep session; and control, based onthe detection of the slow wave activity, the one or more sensorystimulators to deliver the first auditory tones to the subject toincrease the slow wave activity, each of the first auditory tones beingless than one decisecond in length; obtain, via a baseline model,baseline information related to a baseline slow wave activity increasederived from second auditory tones provided to an age matched populationof subjects, wherein the baseline model is configured to provide thebaseline information based on a cumulative number of the second auditorytones delivered to the age matched population; and generate apersonalized model for the subject based on (i) the baselineinformation, (ii) a cumulative number of the first auditory tonesdelivered to the subject, and (iii) a cumulative amount of slow waveactivity of the subject during prior sleep sessions in which the firstauditory tones were provided to the subject, such that the personalizedmodel is configured to provide personalized information related to apersonalized slow wave activity increase derived from the first auditorytones provided to the subject.
 2. A system configured to measure slowwave activity during a sleep session, the system comprising: one or moresensors configured to generate output signals conveying informationrelated to brain activity of a subject during a sleep session; one ormore sensory stimulators configured to provide first sensory stimulationto the subject; and one or more processors coupled to the one or moresensors and the one or more sensory stimulators, the one or moreprocessors configured by machine-readable instructions to: detect, basedon the output signals, slow wave activity of the subject during a sleepsession; and cause, based on the detection of the slow wave activity,the one or more sensory stimulators to deliver the first sensorystimulation to the subject to increase the slow wave activity; obtain,via a baseline model, baseline information related to a baseline slowwave activity increase derived from second sensory stimulation providedto an age matched population of subjects; and generate a personalizedmodel for the subject based on (i) the baseline information, (ii) acumulative amount of the first sensory stimulation delivered to thesubject, and (iii) a cumulative amount of slow wave activity of thesubject during prior sleep sessions in which the first sensorystimulation was provided to the subject, such that the personalizedmodel is configured to provide personalized information related to apersonalized slow wave activity increase derived from the first sensorystimulation provided to the subject.
 3. The system of claim 2, whereinthe baseline model comprises sleep architecture information, secondsensory stimulation information, and cumulative slow wave activityinformation for the age matched population of subjects.
 4. The system ofclaim 2, wherein the slow wave activity of the subject during the sleepsession indicates deep sleep.
 5. The system of claim 2, wherein the oneor more processors are further configured to: detect, based on theoutput signals, slow wave activity of the subject during a plurality ofinitial sleep sessions; store information related to the baseline slowwave activity increase of the age matched population of subjects; andgenerate an initial model describing initial slow wave activityincreases of the subject based on the stored information and the slowwave activity of the subject during the plurality of initial sleepsessions.
 6. The system of claim 5, wherein the information related tothe baseline slow wave activity increase of the age matched populationof subjects is received or preprogrammed.
 7. The system of claim 5,wherein the one or more processors are further configured to providesleep quality feedback to the subject following the plurality of initialsleep sessions based on the baseline model.
 8. The system of claim 5,wherein the one or more processors are further configured to generatethe personalized model based on the initial model.
 9. The system ofclaim 2, wherein the one or more processors are further configured tomodify the personalized model based on the baseline model and the slowwave activity measured by the one or more sensors during the sleepsession.
 10. The system of claim 9, wherein the one or more processorsare further configured to provide sleep quality feedback to the subjectfollowing the sleep session based on the baseline model and the modifiedpersonalized model.
 11. The system of claim 9, wherein the one or moreprocessors are further configured to determine a personalized slow waveactivity increase of the subject in a subsequent sleep session based onthe baseline model and the modified personalized model.
 12. A method formeasuring slow wave activity during a sleep session with a system, thesystem comprising one or more sensors, one or more sensory stimulators,and one or more processors, the method comprising: generating, with theone or more sensors, output signals conveying information related tobrain activity of a subject during the sleep session; detecting, withthe one or more processors, slow wave activity of the subject during asleep session based on the output signals; and causing, based on thedetection of the slow wave activity, the one or more sensory stimulatorsto deliver first sensory stimulation to the subject to increase the slowwave activity; obtaining, via a baseline model, baseline informationrelated to a baseline slow wave activity increase derived from secondsensory stimulation provided to an age matched population of subjects;and generating a personalized model for the subject based on (i) thebaseline information, (ii) a cumulative amount of the first sensorystimulation delivered to the subject, and (iii) a cumulative amount ofslow wave activity of the subject during prior sleep sessions in whichthe first sensory stimulation was provided to the subject, such that thepersonalized model is configured to provide personalized informationrelated to a personalized slow wave activity increase derived from thefirst sensory stimulation provided to the subject.
 13. The method ofclaim 12, wherein the baseline model comprises sleep architectureinformation, second sensory stimulation information, and cumulative slowwave activity information for the age matched population of subjects.14. The method of claim 12, wherein the slow wave activity of thesubject during the sleep session indicates deep sleep.
 15. The method ofclaim 12, further comprising: detecting, with the one or moreprocessors, based on the output signals, slow wave activity of thesubject during a plurality of initial sleep sessions; storing, with theone or more processors, information related to the baseline slow waveactivity increase of the age matched population of subjects; andgenerating, with the one or more processors, an initial model describinginitial slow wave activity increases of the subject based on the storedinformation and the slow wave activity of the subject during theplurality of initial sleep sessions.
 16. The method of claim 15, whereinthe information related to the baseline slow wave activity increase ofthe age matched population of subjects is received or preprogrammed. 17.The method of claim 15, further comprising providing sleep qualityfeedback to the subject following the plurality of initial sleepsessions based on the baseline model.
 18. The method of claim 15,further comprising generating the personalized model based on theinitial model.
 19. The method of claim 12, further comprising modifyingthe personalized model based on the baseline model and the slow waveactivity measured by the one or more sensors during the sleep session.20. The method of claim 19, further comprising providing sleep qualityfeedback to the subject following the sleep session based on thebaseline model and the modified personalized model.
 21. The method ofclaim 19, further comprising determining a personalized slow waveactivity increase of the subject in a subsequent sleep session based onthe baseline model and the modified personalized model.